Contoh ATM

Jika Anda mengetahui berapa banyak kejadian spesifik yang terjadi per satuan ukuran, Anda dapat mengasumsikan bahwa sebaran peubah acak mengikuti sebaran Poisson untuk mempelajari fenomena tersebut.

Pertimbangkan sebuah ATM (automatic teller machine) di pusat perbelanjaan yang sangat ramai. Bank ingin menghindari antrean nasabah untuk menggunakan ATM. Telah diamati bahwa rata-rata jumlah nasabah yang melakukan penarikan antara pukul 10.00 hingga 10.05 pada hari apa pun adalah 1.

Sebagai analis data di bank, Anda diminta menghitung berapa peluang bahwa bank perlu memasang ATM tambahan untuk menangani lonjakan tersebut.

Untuk menjawab pertanyaan ini, Anda perlu menghitung peluang mendapatkan lebih dari satu nasabah selama periode waktu itu.

Latihan ini adalah bagian dari kursus

Dasar-Dasar Probabilitas di Python

Petunjuk latihan

Impor poisson dari scipy.stats.
Hitung peluang terdapat lebih dari satu nasabah yang mengunjungi ATM dalam periode 5 menit ini.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import poisson from scipy.stats
from ____

# Probability of more than 1 customer
probability = ____.____(k=____, mu=____)

# Print the result
print(probability)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Dasar-Dasar Probabilitas di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

A coin flip is the classic example of a random experiment. The possible outcomes are heads or tails. This type of experiment, known as a Bernoulli or binomial trial, allows us to study problems with two possible outcomes, like “yes” or “no” and “vote” or “no vote.” This chapter introduces Bernoulli experiments, binomial distributions to model multiple Bernoulli trials, and probability simulations with the scipy library.

Exercise 1: Let’s flip a coin in Python Exercise 2: Flipping coins Exercise 3: Using binom to flip even more coins Exercise 4: Probability mass and distribution functions Exercise 5: Predicting the probability of defects Exercise 6: Predicting employment status Exercise 7: Predicting burglary conviction rate Exercise 8: Expected value, mean, and variance Exercise 9: Calculating the expected value and variance Exercise 10: Calculating the sample mean Exercise 11: Checking the result Exercise 12: Calculating the mean and variance of a sample

In this chapter you'll learn to calculate various kinds of probabilities, such as the probability of the intersection of two events and the sum of probabilities of two events, and to simulate those situations. You'll also learn about conditional probability and how to apply Bayes' rule.

Exercise 1: Calculating probabilities of two events Exercise 2: Any overlap?Exercise 3: Measuring a sample Exercise 4: Joint probabilities Exercise 5: Deck of cards Exercise 6: Conditional probabilities Exercise 7: Delayed flights Exercise 8: Contingency table Exercise 9: More cards Exercise 10: Total probability law Exercise 11: Formula 1 engines Exercise 12: Voters Exercise 13: Bayes' rule Exercise 14: Conditioning Exercise 15: Factories and parts Exercise 16: Swine flu blood test

Until now we've been working with binomial distributions, but there are many probability distributions a random variable can take. In this chapter we'll introduce three more that are related to the binomial distribution: the normal, Poisson, and geometric distributions.

Exercise 1: Distribusi normal Exercise 2: Rentang nilai Exercise 3: Memplot sebaran normal Exercise 4: Dalam tiga simpangan baku Exercise 5: Probabilitas normal Exercise 6: Contoh pengeluaran restoran Exercise 7: Contoh baterai smartphone Exercise 8: Contoh tinggi orang dewasa Exercise 9: Distribusi Poisson Exercise 10: Contoh ATM

Latihan Saat Ini

Exercise 11: Contoh kecelakaan di jalan tol Exercise 12: Menghasilkan dan memplot distribusi Poisson Exercise 13: Distribusi geometri Exercise 14: Contoh menangkap salmon Exercise 15: Contoh free throw Exercise 16: Menghasilkan dan memplot distribusi geometri

No that you know how to calculate probabilities and important properties of probability distributions, we'll introduce two important results: the law of large numbers and the central limit theorem. This will expand your understanding on how the sample mean converges to the population mean as more data is available and how the sum of random variables behaves under certain conditions. We will also explore connections between linear and logistic regressions as applications of probability and statistics in data science.

Exercise 1: From sample mean to population mean Exercise 2: Generating a sample Exercise 3: Calculating the sample mean Exercise 4: Plotting the sample mean Exercise 5: Adding random variables Exercise 6: Sample means Exercise 7: Sample means follow a normal distribution Exercise 8: Adding dice rolls Exercise 9: Linear regression Exercise 10: Fitting a model Exercise 11: Predicting test scores Exercise 12: Studying residuals Exercise 13: Logistic regression Exercise 14: Fitting a logistic model Exercise 15: Predicting if students will pass Exercise 16: Passing two tests Exercise 17: Wrapping up